Double-stranded RNA-dependent protein kinase, PKR, binds preferentially to Huntington's disease (HD) transcripts and is activated in HD tissue
Bibliographic record
Abstract
Fourteen neurological diseases have been associated with the expansion of trinucleotide repeat regions. These diseases have been categorized into those that give rise to the translation of toxic polyglutamine proteins and those that are untranslated. Thus far, compelling evidence has not surfaced for the inclusion of a model in which a common mechanism may participate in the pathobiology of both translated and untranslated trinucleotide diseases. In these studies we show that a double-stranded RNA-binding protein, PKR, which has previously been linked to virally-induced and stress-mediated apoptosis, preferentially binds mutant huntingtin RNA transcripts immobilized on streptavidin columns that have been incubated with human brain extracts. These studies also show, by immunodetection in tissue slices, that PKR is present in its activated form in both human Huntington autopsy material and brain tissue derived from Huntington yeast artificial chromosome transgenic mice. The increased immunolocalization of the activated kinase is more pronounced in areas most affected by the disease and, coupled with the RNA binding results, suggests a role for PKR activation in the disease process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".